Robust Speech Recognition Using Fuzzy Matrix/Vector Quantisation, Neural Networks and Hidden Markov Models

1999 ◽  
pp. 255-264
Author(s):  
Lin Cong ◽  
Saf Asghar
Keyword(s):  
Neural Networks ◽  
Speech Recognition ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Robust Speech Recognition ◽  
Fuzzy Matrix ◽  
Vector Quantisation ◽  
Matrix Vector

A CONNECTIONIST APPROACH TO SPEECH RECOGNITION

1993 ◽  
Vol 07 (04) ◽  
pp. 647-667 ◽  
Author(s):  
YOSHUA BENGIO
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Speech Recognition ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Statistical Techniques ◽  
Recurrent Networks ◽  
Phoneme Recognition ◽  
Connectionist Approach

The task discussed in this paper is that of learning to map input sequences to output sequences. In particular, problems of phoneme recognition in continuous speech are considered, but most of the discussed techniques could be applied to other tasks, such as the recognition of sequences of handwritten characters. The systems considered in this paper are based on connectionist models, or artificial neural networks, sometimes combined with statistical techniques for recognition of sequences of patterns, stressing the integration of prior knowledge and learning. Different architectures for sequence and speech recognition are reviewed, including recurrent networks as well as hybrid systems involving hidden Markov models.

Sign in / Sign up

Export Citation Format

Share Document